DocumentCode :
2773397
Title :
Subtly different facial expression recognition and expression intensity estimation
Author :
Lien, James Jenn-Jier ; Kanade, Takeo ; Cohn, Jeffrey F. ; Li, Ching-Chung
Author_Institution :
Dept. of Electr. Eng., Pittsburgh Univ., PA, USA
fYear :
1998
fDate :
23-25 Jun 1998
Firstpage :
853
Lastpage :
859
Abstract :
We have developed a computer vision system, including both facial feature extraction and recognition, that automatically discriminates among subtly different facial expressions. Expression classification is based on Facial Action Coding System (FACS) action units (AUs), and discrimination is performed using Hidden Markov Models (HMMs). Three methods are developed to extract facial expression information for automatic recognition. The first method is facial feature point tracking using a coarse-to-fine pyramid method. This method is sensitive to subtle feature motion and is capable of handling large displacements with sub-pixel accuracy. The second method is dense flow tracking together with principal component analysis (PCA) where the entire facial motion information per frame is compressed to a low-dimensional weight vector. The third method is high gradient component (i.e., furrow) analysis in the spatio-temporal domain, which exploits the transient variation associated with the facial expression. Upon extraction of the facial information, non-rigid facial expression is separated from the rigid head motion component, and the face images are automatically aligned and normalized using an affine transformation. This system also provides expression intensity estimation, which has significant effect on the actual meaning of the expression
Keywords :
computer vision; feature extraction; hidden Markov models; action units; affine transformation; coarse-to-fine pyramid method; computer vision system; expression classification; expression intensity estimation; facial action coding system; facial feature extraction; facial feature point tracking; facial motion information; hidden Markov models; principal component analysis; rigid head motion component; subtly different facial expression recognition; Computer vision; Data mining; Face recognition; Facial features; Hidden Markov models; Image coding; Motion analysis; Principal component analysis; Tracking; Transient analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on
Conference_Location :
Santa Barbara, CA
ISSN :
1063-6919
Print_ISBN :
0-8186-8497-6
Type :
conf
DOI :
10.1109/CVPR.1998.698704
Filename :
698704
Link To Document :
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